Jargon is an innovative Chrome extension (Chrome Web Store, Official Website) created by my friend that transforms English web content into learning opportunities using generative AI technology. Launched in June 2024, Jargon offers two types of learning experiences: foreign language learning (Spanish, Chinese, etc.) and English style adaptation (GRE vocabulary, TikTok slang, etc.).
Figure 1: User Settings Interface showing customization options
Key Features
Language Selection
All types, from foreign languages like Spanish and Chinese to English variations such as TikTok Slang
Learning Goals
• Difficulty: Easy-Hard (1-10)
• Daily Target: 10-100
questions
Question Density
Controls percentage of eligible sentences (0-100%) highlighted for practice on each webpage
Display Settings
• Text Style: Highlight or underline
• Site Controls:
Enable/disable per website or temporarily
Figure 2a: Highlight Style - Text appears with background color emphasis
Figure 2b: Underline Style - Text appears with underline emphasis
Figure 3: Question Generation Process - Users select text from any webpage to create practice questions
The GRE mode enhances vocabulary learning by replacing common words with their more sophisticated alternatives (e.g., “good” becomes “exemplary”), while TikTok style transforms formal English into contemporary social media expressions (e.g., “That’s cool” becomes “That’s bussin fr fr”). These AI-powered transformations maintain the original meaning while adapting to different language registers.
After 10 months of operation and 93 users, this analysis investigates three key aspects of user behavior:
The data for this analysis was collected from Jargon’s Supabase database, covering user interactions from the extension’s launch in June 2024 through March 16, 2025. The dataset comprises five main tables:
| Dataset | Records | Description |
|---|---|---|
| Profiles | 92 | User profiles and settings |
| Questions | 2442 | Generated practice questions |
| Words | 1594 | Vocabulary entries and translations |
| Levels | 117 | User progression through difficulty levels |
| Websites | 27 | Websites where extension was disabled |
| Variable | Type | Description | Notes |
|---|---|---|---|
| user_id | Primary Key | Unique identifier for each user | Anonymized identifier |
| level | Integer | Current proficiency level | Range: 1-10 |
| paused | Boolean | Extension status on Chrome | TRUE/FALSE (Default: TRUE) |
| chrome_notifs | Boolean | Notification preferences | TRUE/FALSE |
| language | String | Current selected language mode | e.g., ‘GRE Vocabulary’, ‘TikTok Slang’ |
| last_question_time | DateTime | Timestamp of most recent question | UTC timezone |
| week_streak | Integer | Consecutive weeks of activity | |
| daily_streak | Integer | Consecutive days of activity | |
| daily_progress | Integer | Questions completed today | Resets daily |
| daily_goal | Integer | Target questions per day | User-set goal |
| density | Integer | Frequency of questions | Percentage of eligible sentences shown (0-100) |
| highlightStyle | String | Text selection preference | ‘highlight’ or ‘underline’ |
| Variable | Type | Description | Notes |
|---|---|---|---|
| question_id | Primary Key | Unique question identifier | |
| user_id | Foreign Key | Associated user | References profiles |
| created_at | DateTime | Question generation time | UTC timezone |
| sentence | Text | Original selected text | English source content |
| word | String | Target word for learning | |
| language | String | Transformation mode | Selected language mode |
| original_sentence | Text | Source text | Pre-transformation content |
| options_array | Array of String | Multiple choice options | Even indices: options in target language; Odd indices: English translations |
| answered_at | DateTime | Completion timestamp | NULL if unanswered |
| chosen_option | String | User’s answer | NULL if unanswered |
| user_rating | Integer | Question quality rating | Feature not yet implemented |
| Variable | Type | Description | Notes |
|---|---|---|---|
| created_at | DateTime | Word entry timestamp | UTC timezone |
| word | String | Target vocabulary | |
| language | String | Language mode | |
| user_id | Foreign Key | Associated user | References profiles |
| translation | Text | English translation | AI-generated translation |
| status | String | Learning status | Currently all set to ‘learning’ |
| Variable | Type | Description | Notes |
|---|---|---|---|
| user_id | Foreign Key | Associated user | References profiles |
| language | String | Language mode | |
| level | Integer | Difficulty level | Range: 1-10 |
| Variable | Type | Description | Notes |
|---|---|---|---|
| user_id | Foreign Key | Associated user | References profiles |
| website | String | Blocked URL | Sites where Jargon is disabled |
Profile Enhancement
| Variable | Calculation | Purpose |
|---|---|---|
| generated_questions | Count of questions per user | Measure overall engagement |
| answered_questions | Count of questions with answers | Measure learning completion |
| blocked_sites | Count of blocked websites | Understand avoidance patterns |
| levels_attempted | Count of unique combination of languages and difficulty levels | Track learning progression |
This analysis adopts a sequential approach to address each research question independently, allowing for focused exploration and detailed insights. We first investigate the usage context and platform patterns by analyzing website blocking behavior and user interaction patterns. Following this comprehensive examination of the first question, we then explore feature adoption patterns and their relationship to user success, focusing on how different customization choices correlate with engagement levels.
Our exploratory data analysis examines patterns that inform both research questions about usage context and feature adoption. We organize our exploration into three main categories:
[Relevant to Research Question 1: Usage Context and Platform Patterns]
Figure 5: Website Usage Analysis - Distribution of blocked websites by category (left) and frequency of individual websites (right)
The analysis of blocked websites reveals distinct patterns in how users interact with the Jargon extension. Professional tools, particularly Salesforce and AI platforms, emerge as the most frequently blocked categories, suggesting that users primarily utilize Jargon during work-related activities. The presence of development environment blocks in the dataset indicates that the user base includes some technical professionals, though this represents a modest portion of the overall usage. Educational content also features prominently in the blocked websites, with users frequently disabling the extension on documentation sites and educational platforms, possibly to maintain focus during concentrated learning sessions. Interestingly, social media platforms show lower blocking rates than initially hypothesized, with users selectively choosing which major platforms to exclude from Jargon’s functionality, rather than implementing broad-based social media blocks.
[Relevant to Both Research Questions]
Figure 6: Scatter plot showing the relationship between user adoption and question generation across different language modes
The scatter plot reveals several key insights about language mode usage patterns:
These patterns suggest that while traditional language learning drives most user activity, there’s significant variation in how different language modes are utilized, with some showing intense usage by small groups while others have broader but less intensive adoption.
Figure 7: Word frequency analysis showing common words (left) and word pairs (right) in learning content. Colors indicate frequency of occurrence, with darker shades representing higher frequencies.
The word frequency analysis reveals patterns in user-selected content:
These patterns suggest users often engage with technical and descriptive content, particularly in scientific or educational contexts.
[Relevant to Both Research Questions]
Figure 8: Daily activity patterns showing question generation and active users with their respective averages (dashed lines) over the observation period, based on UTC timezone. Questions average: 12.5 per day; Users average: 2.2 per day.
Figure 9: Weekly activity patterns showing average questions generated and active users by day of week (UTC timezone), with error bars indicating standard error.
The temporal analysis reveals several key patterns (Note: All timestamps are in UTC, which may shift actual usage patterns by several hours depending on users’ local time zones):
Note: Future analysis would benefit from timezone-adjusted data to more accurately reflect users’ local activity patterns.
[Relevant to Research Question 2: Feature Adoption and User Success]
Figure 10: Distribution of key engagement metrics across users, showing individual box plots for each metric with median and interquartile range (IQR) statistics. Each plot uses a distinct color and includes summary statistics.
The distribution of engagement metrics reveals distinct patterns in user behavior:
These patterns suggest a typical engagement profile of moderate, focused activity with a distinct subset of highly engaged users.
[Current website analysis content]
[To be developed]
Figure 11: Correlation matrix of user engagement features. Circle size and color intensity indicate correlation strength, with blue showing positive correlations and red showing negative correlations.
The correlation analysis reveals several key relationships between feature adoption and user engagement:
These findings provide strong evidence that certain feature combinations and usage patterns are associated with higher engagement levels, particularly: - The choice of highlight style over underlining - Active customization of the learning environment - Exploration of multiple language levels - Consistent completion of generated questions
[Additional sections on feature adoption patterns…]